A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation-based Synthetic Contrast Augmentation

Purpose: To design and evaluate an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps with use of synthetic T1-weighted images for MRI relaxation-based contrast augmentation.Materials and Methods: This retrospective study included MRI scans acquired between 2016 and 2019 from 100 patients (mean age & PLUSMN; SD, 55 years & PLUSMN; 13; 72 men) across various clinical abnormalities with use of a modified Look-Locker inversion recovery, or MOLLI, sequence to quantify native T1 (T1native), postcontrast T1 (T1post),and extracellular volume (ECV). Data were divided into training (n = 60) and internal (n = 40) test subsets. Synthetic T1-weighted images were generated from the T1 exponential inversion-recovery signal model at a range of optimal inversion times, yielding high blood-myocardium contrast, and were used for contrast-based image augmentation during training and testing of a convolutional neural network for myocardial segmentation. Automated segmentation, T1, and ECV were compared with experts with use of Dice similarity coefficients (DSCs), correlation coefficients, and Bland-Altman analysis. An external test dataset (n = 147) was used to assess model generalization.Results: Internal testing showed high myocardial DSC relative to experts (0.81 & PLUSMN; 0.08), which was similar to interobserver DSC (0.81 & PLUSMN; 0.08). Automated segmental measurements strongly correlated with experts (T1native ,R = 0.87; T1post , R = 0.91; ECV, R = 0.92), which were similar to interobserver correlation (T1native , R = 0.86; T1post , R = 0.94; ECV, R = 0.95). External testing showed strong DSC (0.80 & PLUSMN; 0.09) and T1native correlation (R = 0.88) between automatic and expert analysis.Conclusion: This deep learning method leveraging synthetic contrast augmentation may provide accurate automated T1 and ECV analy-sis for cardiac MRI data acquired across different abnormalities, centers, scanners, and T1 sequences.Supplemental material is available for this article.& COPY; RSNA, 2022

Automated summary

This study designed and evaluated an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps using synthetic T1-weighted images. The method showed accurate results in T1 and ECV analysis across different abnormalities, centers, scanners, and T1 sequences.